Stochastic Control in Insurance and Finance: Modelling and Numerical Analysis

Dear Colleagues,

Stochastic control theory is a powerful tool to investigate the decision-making problems arising from insurance and finance. Different assumptions regarding the control variables lead to various optimization formulations. Depending on the complexity of the stochastic systems, analytical derivation and numerical methods—especially the recent breakthrough machine learning techniques—represent alternative approaches to tackling problems.

This Special Issue aims to collect high-quality research papers on theoretical and numerical methods for solving stochastic optimization problems in insurance and finance. We encourage submissions that are related, but not limited, to the following topics:

• Dividend
• Reinsurance
• Optimal investment/consumption
• Retirement planning
• Optimal stopping
• Optimal contracting
• Risk sharing
• Stochastic games
• Machine learning methods
• Computational methods in stochastic systems

Dr. Zhuo Jin
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.